Ricerc@Sapienza

The modern revolution in information technology, which has been significantly impacted by the advent of Internet-of-Things, has allowed us to interact in advanced ways with a plethora of mobile devices, technological artefacts embedded in the surrounding environment, and entire systems. It is hard to deny that the "user interaction everywhere" phenomenon has changed our way of living and relating. The general feeling is that interactivity is a positive feature, associated with being flexible, informed, and in control.

Among the most relevant aspects of a user interaction, this research projects focuses on the concept of learnability, which applies to the nature of the performance change of a user when interacting with a system to accomplish a specific task. The interest in defining and measuring learnability of interactive systems is acknowledged by the presence of several research studies that are available (from more than four decades) in the Human-Computer Interaction literature. Nevertheless, to date, there is no a shared understanding of the concept of learnability and, as a consequence, several accepted metrics exist to assess the initial learnability of a system, which represents (at best) a measure of the system's intuitiveness and is not necessarily an indicator of continued learning.

This research project starts from the idea that measuring the degree of learnability of a system over time (the extended learnability) is crucial for understanding exactly what happens during user interactions and for designing transparent, explainable and interpretable interactive systems. To tackle this challenge, this project investigates how a targeted use of techniques and algorithms from the Process Mining community provides a natural framework for: (i) discovering interaction models that precisely describe user interactions; (ii) measuring if an interactive system is learnable with respect to its tasks, by detecting potential learning issues and ways to fix them.